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Prior knowledge snake segmentation of ultrasound images denoised by J ‐divergence anisotropy diffusion
Author(s) -
Yan Jiawen,
Pan Bo,
Qi Yunfeng,
Ben Jin,
Fu Yili
Publication year - 2018
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1924
Subject(s) - computer science , imaging phantom , artificial intelligence , ultrasound , segmentation , computer vision , forceps , divergence (linguistics) , orientation (vector space) , anisotropic diffusion , laparoscopic radical prostatectomy , radiology , image (mathematics) , prostatectomy , medicine , mathematics , prostate , surgery , linguistics , philosophy , geometry , cancer
Background Applying transrectal ultrasound to robot‐assisted laparoscopic radical prostatectomy has attracted attention in recent years, and it is considered as a proper method to provide real‐time subsurface anatomic features. A precise registration between the ultrasound equipment and robotic surgical system is necessary, which usually requires a fast and accurate recognition of the registration tool in the ultrasound image. Methods Tissue forceps are chosen as the registration tool. J ‐divergence anisotropy diffusion and prior knowledge snake segmentation are proposed for the automatic recognition of forceps in ultrasound images. Results Simulation, gel tissue phantom experiments and in vitro experiments are carried out. Several evaluation indices are calculated to compare results under different methods. Conclusions The proposed methods are proved to be practicable, reliable and superior to existing ones, with reduced calculation time and higher accuracy.